OpenRaiser/Intern-Atlas huggingface dataset:
Iterative Deep Convolutional Encoder-Decoder Network for Medical Image Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning. Experimental results show that the proposed iterative deep learning framework is able to yield excellent medical image segmentation performances for various medical images. The effectiveness of the proposed method has been proved by comparing with other state-of-the-art medical image segmentation methods.
New Tools and Connections for Exponential-time Approximation
Venue: arXiv cs.DS 2017
Year: 2017

In this paper, we develop new tools and connections for exponential time approximation. In this setting, we are given a problem instance and a parameter $α>1$, and the goal is to design an $α$-approximation algorithm with the fastest possible running time. We show the following results: - An $r$-approximation for maximum independent set in $O^*(\exp(\tilde O(n/r \log^2 r+r\log^2r)))$ time, - An $r$-approximation for chromatic number in $O^*(\exp(\tilde{O}(n/r \log r+r\log^2r)))$ time, - A $(2-1/r)$-approximation for minimum vertex cover in $O^*(\exp(n/r^{Ω(r)}))$ time, and - A $(k-1/r)$-approximation for minimum $k$-hypergraph vertex cover in $O^*(\exp(n/(kr)^{Ω(kr)}))$ time. (Throughout, $\tilde O$ and $O^*$ omit $\mathrm{polyloglog}(r)$ and factors polynomial in the input size, respectively.) The best known time bounds for all problems were $O^*(2^{n/r})$ [Bourgeois et al. 2009, 2011 & Cygan et al. 2008]. For maximum independent set and chromatic number, these bounds were complemented by $\exp(n^{1-o(1)}/r^{1+o(1)})$ lower bounds (under the Exponential Time Hypothesis (ETH)) [Chalermsook et al., 2013 & Laekhanukit, 2014 (Ph.D. Thesis)]. Our results show that the naturally-looking $O^*(2^{n/r})$ bounds are not tight for all these problems. The key to these algorithmic results is a sparsification procedure, allowing the use of better approximation algorithms for bounded degree graphs. For obtaining the first two results, we introduce a new randomized branching rule. Finally, we show a connection between PCP parameters and exponential-time approximation algorithms. This connection together with our independent set algorithm refute the possibility to overly reduce the size of Chan's PCP [Chan, 2016]. It also implies that a (significant) improvement over our result will refute the gap-ETH conjecture [Dinur 2016 & Manurangsi and Raghavendra, 2016].
Deep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1
Venue: arXiv cs.CV 2017
Year: 2017

Mapping winter vegetation quality coverage is a challenge problem of remote sensing. This is due to the cloud coverage in winter period, leading to use radar rather than optical images. The objective of this paper is to provide a better understanding of the capabilities of radar Sentinel-1 and deep learning concerning about mapping winter vegetation quality coverage. The analysis presented in this paper is carried out on multi-temporal Sentinel-1 data over the site of La Rochelle, France, during the campaign in December 2016. This dataset were processed in order to produce an intensity radar data stack from October 2016 to February 2017. Two deep Recurrent Neural Network (RNN) based classifier methods were employed. We found that the results of RNNs clearly outperformed the classical machine learning approaches (Support Vector Machine and Random Forest). This study confirms that the time series radar Sentinel-1 and RNNs could be exploited for winter vegetation quality cover mapping.
Generalized Graph Pattern Matching
Venue: arXiv cs.DB 2017
Year: 2017

Most of the machine learning algorithms are limited to learn from flat data: a recordset with prefixed structure. When learning from a record, these types of algorithms don't take into account other objects even though they are directly connected to it and can provide valuable information for the learning task. In this paper we present the concept of Generalized Graph Query, a query tool over graphs or multi-relational data structures. They are built using the same graph structure as generalized graphs and allow to express powerful relational and non-relational restrictions on this type of data. Also, this paper shows mechanisms to build this kind of queries dynamically and how they can be used to perform bottom-up discovery processes through machine laerning techniques. ----- La mayoría de los algoritmos que aprenden a partir de datos están limitados ya que sólo son capaces de aprender a partir de datos estructurados en forma de tabla en la que cada fila representa un registro y cada columna una propiedad asociada. Estos algoritmos, no tienen en cuenta los atributos de las estructuras con las que un registro dado puede estar relacionado, a pesar de que éstos pueden aportar información útil a la hora de llevar a cabo la tarea de aprendizaje. En este artículo presentamos el concepto de Generalized Graph Query, una herramienta de consulta de patrones en grafos generalizados. Dicha herramienta ha sido construida utilizando la estructura de Grafo Generalizado y permite expresar restricciones relacionales y no relacionales sobre este tipo de estructuras. Además, en este artículo se presentan mecanismos para la construcción automática de este tipo de consultas y se muestra cómo éstas pueden ser utilizadas en procesos de descubrimiento tipo bottom-up a través de técnicas relacionadas con el Aprendizaje Automático.
5G Spectrum Sharing
Venue: arXiv cs.NI 2017
Year: 2017

In this paper an overview is given of the current status of 5G industry standards, spectrum allocation and use cases, followed by initial investigations of new opportunities for spectrum sharing in 5G using cognitive radio techniques, considering both licensed and unlicensed scenarios. A particular attention is given to sharing millimeter-wave frequencies, which are of prominent importance for 5G.
Chainspace: A Sharded Smart Contracts Platform
Venue: arXiv cs.CR 2017
Year: 2017

Chainspace is a decentralized infrastructure, known as a distributed ledger, that supports user defined smart contracts and executes user-supplied transactions on their objects. The correct execution of smart contract transactions is verifiable by all. The system is scalable, by sharding state and the execution of transactions, and using S-BAC, a distributed commit protocol, to guarantee consistency. Chainspace is secure against subsets of nodes trying to compromise its integrity or availability properties through Byzantine Fault Tolerance (BFT), and extremely high-auditability, non-repudiation and `blockchain' techniques. Even when BFT fails, auditing mechanisms are in place to trace malicious participants. We present the design, rationale, and details of Chainspace; we argue through evaluating an implementation of the system about its scaling and other features; we illustrate a number of privacy-friendly smart contracts for smart metering, polling and banking and measure their performance.
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
Venue: arXiv cs.RO 2017
Year: 2017

A monocular visual-inertial system (VINS), consisting of a camera and a low-cost inertial measurement unit (IMU), forms the minimum sensor suite for metric six degrees-of-freedom (DOF) state estimation. However, the lack of direct distance measurement poses significant challenges in terms of IMU processing, estimator initialization, extrinsic calibration, and nonlinear optimization. In this work, we present VINS-Mono: a robust and versatile monocular visual-inertial state estimator.Our approach starts with a robust procedure for estimator initialization and failure recovery. A tightly-coupled, nonlinear optimization-based method is used to obtain high accuracy visual-inertial odometry by fusing pre-integrated IMU measurements and feature observations. A loop detection module, in combination with our tightly-coupled formulation, enables relocalization with minimum computation overhead.We additionally perform four degrees-of-freedom pose graph optimization to enforce global consistency. We validate the performance of our system on public datasets and real-world experiments and compare against other state-of-the-art algorithms. We also perform onboard closed-loop autonomous flight on the MAV platform and port the algorithm to an iOS-based demonstration. We highlight that the proposed work is a reliable, complete, and versatile system that is applicable for different applications that require high accuracy localization. We open source our implementations for both PCs and iOS mobile devices.
Image Quality Assessment Guided Deep Neural Networks Training
Venue: arXiv cs.CV 2017
Year: 2017

For many computer vision problems, the deep neural networks are trained and validated based on the assumption that the input images are pristine (i.e., artifact-free). However, digital images are subject to a wide range of distortions in real application scenarios, while the practical issues regarding image quality in high level visual information understanding have been largely ignored. In this paper, in view of the fact that most widely deployed deep learning models are susceptible to various image distortions, the distorted images are involved for data augmentation in the deep neural network training process to learn a reliable model for practical applications. In particular, an image quality assessment based label smoothing method, which aims at regularizing the label distribution of training images, is further proposed to tune the objective functions in learning the neural network. Experimental results show that the proposed method is effective in dealing with both low and high quality images in the typical image classification task.
Dynamic Minimum Spanning Forest with Subpolynomial Worst-case Update Time
Venue: arXiv cs.DS 2017
Year: 2017

We present a Las Vegas algorithm for dynamically maintaining a minimum spanning forest of an $n$-node graph undergoing edge insertions and deletions. Our algorithm guarantees an $O(n^{o(1)})$ worst-case update time with high probability. This significantly improves the two recent Las Vegas algorithms by Wulff-Nilsen [STOC'17] with update time $O(n^{0.5-ε})$ for some constant $ε>0$ and, independently, by Nanongkai and Saranurak [STOC'17] with update time $O(n^{0.494})$ (the latter works only for maintaining a spanning forest). Our result is obtained by identifying the common framework that both two previous algorithms rely on, and then improve and combine the ideas from both works. There are two main algorithmic components of the framework that are newly improved and critical for obtaining our result. First, we improve the update time from $O(n^{0.5-ε})$ in Wulff-Nilsen [STOC'17] to $O(n^{o(1)})$ for decrementally removing all low-conductance cuts in an expander undergoing edge deletions. Second, by revisiting the "contraction technique" by Henzinger and King [1997] and Holm et al. [STOC'98], we show a new approach for maintaining a minimum spanning forest in connected graphs with very few (at most $(1+o(1))n$) edges. This significantly improves the previous approach in [Wulff-Nilsen STOC'17] and [Nanongkai and Saranurak STOC'17] which is based on Frederickson's 2-dimensional topology tree and illustrates a new application to this old technique.
Group-driven Reinforcement Learning for Personalized mHealth Intervention
Venue: arXiv cs.LG 2017
Year: 2017

Due to the popularity of smartphones and wearable devices nowadays, mobile health (mHealth) technologies are promising to bring positive and wide impacts on people's health. State-of-the-art decision-making methods for mHealth rely on some ideal assumptions. Those methods either assume that the users are completely homogenous or completely heterogeneous. However, in reality, a user might be similar with some, but not all, users. In this paper, we propose a novel group-driven reinforcement learning method for the mHealth. We aim to understand how to share information among similar users to better convert the limited user information into sharper learned RL policies. Specifically, we employ the K-means clustering method to group users based on their trajectory information similarity and learn a shared RL policy for each group. Extensive experiment results have shown that our method can achieve clear gains over the state-of-the-art RL methods for mHealth.
Pilot Contamination for Wideband Massive MMO: Number of cells Vs Multipath
Venue: arXiv cs.IT 2017
Year: 2017

This paper proposes a novel joint channel estimation and beamforming approach for multicell wideband massive multiple input multiple output (MIMO) systems. With the proposed channel estimation and beamforming approach, we determine the number of cells $N_c$ that can utilize the same time and frequency resource while mitigating the effect of pilot contamination. The proposed approach exploits the multipath characteristics of wideband channels. Specifically, when the channel has a maximum of $L$ multipath taps, \textcolor{red}{it is shown that $N_c\leq L$ cells can estimate the channels of their user equipments (UEs) and perform beamforming while mitigating the effect of pilot contamination.} In a typical long term evolution (LTE) channel environment having delay spread $T_d=4.69μ$ second and channel bandwidth $B=2.5$MHz, we have found that $L=18$ cells can use this band. In practice, $T_d$ is constant for a particular environment and carrier frequency, and hence $L$ increases as the bandwidth increases. All the analytical expressions have been validated, and the superiority of the proposed design over the existing ones is demonstrated using extensive numerical simulations both for correlated and uncorrelated channels. The proposed channel estimation and beamforming design is linear and simple to implement.
Counterexample Guided Inductive Optimization Applied to Mobile Robots Path Planning (Extended Version)
Venue: arXiv cs.RO 2017
Year: 2017

We describe and evaluate a novel optimization-based off-line path planning algorithm for mobile robots based on the Counterexample-Guided Inductive Optimization (CEGIO) technique. CEGIO iteratively employs counterexamples generated from Boolean Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) solvers, in order to guide the optimization process and to ensure global optimization. This paper marks the first application of CEGIO for planning mobile robot path. In particular, CEGIO has been successfully applied to obtain optimal two-dimensional paths for autonomous mobile robots using off-the-shelf SAT and SMT solvers.
Link Classification and Tie Strength Ranking in Online Social Networks with Exogenous Interaction Networks
Venue: arXiv cs.SI 2017
Year: 2017

Online social networks (OSNs) have become the main medium for connecting people, sharing knowledge and information, and for communication. The social connections between people using these OSNs are formed as virtual links (e.g., friendship and following connections) that connect people. These links are the heart of today's OSNs as they facilitate all of the activities that the members of a social network can do. However, many of these networks suffer from noisy links, i.e., links that do not reflect a real relationship or links that have a low intensity, that change the structure of the network and prevent accurate analysis of these networks. Hence, a process for assessing and ranking the links in a social network is crucial in order to sustain a healthy and real network. Here, we define link assessment as the process of identifying noisy and non-noisy links in a network. In this paper, we address the problem of link assessment and link ranking in social networks using external interaction networks. In addition to a friendship social network, additional exogenous interaction networks are utilized to make the assessment process more meaningful. We employed machine learning classifiers for assessing and ranking the links in the social network of interest using the data from exogenous interaction networks. The method was tested with two different datasets, each containing the social network of interest, with the ground truth, along with the exogenous interaction networks. The results show that it is possible to effectively assess the links of a social network using only the structure of a single network of the exogenous interaction networks, and also using the structure of the whole set of exogenous interaction networks. The experiments showed that some classifiers do better than others regarding both link classification and link ranking.
Early Improving Recurrent Elastic Highway Network
Venue: arXiv cs.LG 2017
Year: 2017

To model time-varying nonlinear temporal dynamics in sequential data, a recurrent network capable of varying and adjusting the recurrence depth between input intervals is examined. The recurrence depth is extended by several intermediate hidden state units, and the weight parameters involved in determining these units are dynamically calculated. The motivation behind the paper lies on overcoming a deficiency in Recurrent Highway Networks and improving their performances which are currently at the forefront of RNNs: 1) Determining the appropriate number of recurrent depth in RHN for different tasks is a huge burden and just setting it to a large number is computationally wasteful with possible repercussion in terms of performance degradation and high latency. Expanding on the idea of adaptive computation time (ACT), with the use of an elastic gate in the form of a rectified exponentially decreasing function taking on as arguments as previous hidden state and input, the proposed model is able to evaluate the appropriate recurrent depth for each input. The rectified gating function enables the most significant intermediate hidden state updates to come early such that significant performance gain is achieved early. 2) Updating the weights from that of previous intermediate layer offers a richer representation than the use of shared weights across all intermediate recurrence layers. The weight update procedure is just an expansion of the idea underlying hypernetworks. To substantiate the effectiveness of the proposed network, we conducted three experiments: regression on synthetic data, human activity recognition, and language modeling on the Penn Treebank dataset. The proposed networks showed better performance than other state-of-the-art recurrent networks in all three experiments.
Numerical simulation of oxidation processes in a cross-flow around tube bundles
Venue: arXiv cs.CE 2017
Year: 2017

An oxidation process is simulated for a bundle of metal tubes in a cross-flow. A fluid flow is governed by the incompressible Navier-Stokes equations. To describe the transport of oxygen, the corresponding convection-diffusion equation is applied. The key point of the model is related to the description of oxidation processes taking into account the growth of a thin oxide film in the quasi-stationary approximation. Mathematical modeling of oxidant transport in a tube bundle is carried out in the 2D approximation. The numerical algorithm employed in the work is based on the finite-element discretization in space and the fully implicit discretization in time. The tube rows of a bundle can be either in-line or staggered in the direction of the fluid flow velocity. The growth of the oxide film on tube walls is predicted for various bundle structures using the developed oxidation model.
Strange Attractor for Efficient Polar Code Design
Venue: arXiv cs.IT 2017
Year: 2017

This paper presents a definition of a construction for long polar codes. Recently, we know that partial order is a universal property of the construction with a sublinear complexity for polar codes. In order to describe the partial order, addition and left-swap operators are only defined as universal up to now. In this study, we first propose $1+\log_2 \log_2 N$ universal operators to describe multiple partial order for the block length $N=2^n$. By using these operators, some known antichains can be universally ordered. Furthermore, by using a simple geometric property of Gaussian approximation, we define an attractor that is a pre-defined subset of synthetic channels. They are universally less reliable than the natural channel $W$. Then, we show that the cardinality of this attractor is $(n+2)$-th Fibonacci number which is a significantly large number of channels for long codes. The main contribution is that there are significant number of synthetic channels explicitly defined as almost useless by the help of attractor and multiple partial order. As a result, proposed attractor with multiple partial order can be seen as an efficient tool to investigate and design extremely long codes.
Deep Object-Centric Representations for Generalizable Robot Learning
Venue: arXiv cs.RO 2017
Year: 2017

Robotic manipulation in complex open-world scenarios requires both reliable physical manipulation skills and effective and generalizable perception. In this paper, we propose a method where general purpose pretrained visual models serve as an object-centric prior for the perception system of a learned policy. We devise an object-level attentional mechanism that can be used to determine relevant objects from a few trajectories or demonstrations, and then immediately incorporate those objects into a learned policy. A task-independent meta-attention locates possible objects in the scene, and a task-specific attention identifies which objects are predictive of the trajectories. The scope of the task-specific attention is easily adjusted by showing demonstrations with distractor objects or with diverse relevant objects. Our results indicate that this approach exhibits good generalization across object instances using very few samples, and can be used to learn a variety of manipulation tasks using reinforcement learning.
Fast, large-scale hologram calculation in wavelet domain
Venue: arXiv cs.GR 2017
Year: 2017

We propose a large-scale hologram calculation using WAvelet ShrinkAge-Based superpositIon (WASABI), a wavelet transform-based algorithm. An image-type hologram calculated using the WASABI method is printed on a glass substrate with the resolution of $65,536 \times 65,536$ pixels and a pixel pitch of $1 μ$m. The hologram calculation time amounts to approximately 354 s on a commercial CPU, which is approximately 30 times faster than conventional methods.
Sampling High Throughput Data for Anomaly Detection of Data-Base Activity
Venue: arXiv cs.CR 2017
Year: 2017

Data leakage and theft from databases is a dangerous threat to organizations. Data Security and Data Privacy protection systems (DSDP) monitor data access and usage to identify leakage or suspicious activities that should be investigated. Because of the high velocity nature of database systems, such systems audit only a portion of the vast number of transactions that take place. Anomalies are investigated by a Security Officer (SO) in order to choose the proper response. In this paper we investigate the effect of sampling methods based on the risk the transaction poses and propose a new method for "combined sampling" for capturing a more varied sample.